标题： 一个灵活的贝叶斯非参数混合模型揭示了视觉工作记忆中交换错误的多重依赖关系 显示英文标题

标题： A flexible Bayesian non-parametric mixture model reveals multiple dependencies of swap errors in visual working memory

摘要： 人类行为数据在心理物理学中已被用来阐明许多认知过程的潜在机制，如注意力、感觉运动整合和感知决策。视觉工作记忆尤其从这种方法中受益：对视觉工作记忆错误的分析对于理解视觉工作记忆容量和编码方案至关重要，进而限制了神经模型的发展。 一类视觉工作记忆错误理解得还不够充分，即交换错误，这指的是参与者回忆起未被提示的记忆项目。交换错误可能源于错误的记忆编码、存储噪声或检索时的错误——先前的研究主要归因于后两种情况。 然而，这些研究对这些来源所贡献的详细机制和/或误差的参数形式做了强烈的先验假设。 在这里，我们采取了一种数据驱动的方法，引入了一个贝叶斯非参数混合模型来描述交换错误（BNS），该模型提供了一个灵活的描述性模型来描述交换行为，使得交换可以依赖于每个刺激项目的探查特征和报告特征。 我们将BNS模型拟合到人类参与者的逐次试验行为，并表明它能够重现多个数据集中交换与提示相似性的强烈依赖关系。 关键的是，BNS揭示了这种依赖关系与随机点运动方向提示、位置报告的数据集在报告特征维度上的非单调调制共存。 由BNS推断出的调制形式提出了新的问题，关于记忆编码在导致视觉工作记忆中的交换错误中的重要性，这是一个与之前提出的绑定和提示错误不同的新来源。 我们的分析结合了对高度可解释的BNS参数结构的定性比较以及严格的定量模型比较和恢复方法，表明以前对交换错误的解释可能是不完整的。 显示英文摘要

摘要： Human behavioural data in psychophysics has been used to elucidate the underlying mechanisms of many cognitive processes, such as attention, sensorimotor integration, and perceptual decision making. Visual working memory has particularly benefited from this approach: analyses of VWM errors have proven crucial for understanding VWM capacity and coding schemes, in turn constraining neural models of both. One poorly understood class of VWM errors are swap errors, whereby participants recall an uncued item from memory. Swap errors could arise from erroneous memory encoding, noisy storage, or errors at retrieval time - previous research has mostly implicated the latter two. However, these studies made strong a priori assumptions on the detailed mechanisms and/or parametric form of errors contributed by these sources. Here, we pursue a data-driven approach instead, introducing a Bayesian non-parametric mixture model of swap errors (BNS) which provides a flexible descriptive model of swapping behaviour, such that swaps are allowed to depend on both the probed and reported features of every stimulus item. We fit BNS to the trial-by-trial behaviour of human participants and show that it recapitulates the strong dependence of swaps on cue similarity in multiple datasets. Critically, BNS reveals that this dependence coexists with a non-monotonic modulation in the report feature dimension for a random dot motion direction-cued, location-reported dataset. The form of the modulation inferred by BNS opens new questions about the importance of memory encoding in causing swap errors in VWM, a distinct source to the previously suggested binding and cueing errors. Our analyses, combining qualitative comparisons of the highly interpretable BNS parameter structure with rigorous quantitative model comparison and recovery methods, show that previous interpretations of swap errors may have been incomplete.